Color mapping determination for an N-color marking device based upon image spatial noise defects

ABSTRACT

What is disclosed is a novel system and method for determining color profiles based upon optimizing output image spatial noise. For each of a number of selected output colors, spatial noise values for a set of device-dependent color specifications that produce the selected output color are iteratively determined. The set of device-dependent color specifications is generated by varying a subset of colorants in the device-dependent color specifications while changing the remaining colorants to maintain the selected output color. The iterative process improves the spatial noise value, as determined by a spatial noise model, of the device-dependent color specifications that correspond to the selected output color. When an optimum spatial noise value is found, the device-dependent color specification having that spatial noise value is selected as the mapping for the selected device-independent color specification. Various embodiments are disclosed.

TECHNICAL FIELD

The present invention is directed to systems and methods to determinedevice-independent color specification to device-dependent colorspecification mapping based upon a method to improve color image spatialnoise defects in N-color marking devices where N≧4.

BACKGROUND

In digital imaging systems, color management is the controlledconversion between the color representations of various devices, such asimage scanners, digital cameras, monitors, TV screens, film printers,office printers, offset presses, corresponding media, and the like. Oneprimary goal of color management is to obtain a good match acrossdifferent color devices. For example, a video should appear the samecolor when displayed on a computer LCD monitor, a plasma TV screen, andon a printed frame of that video. Color management helps achieve a samecolor appearance across a variety of devices, provided the devices arecapable of delivering the needed color intensities. One cross-platformview of color management is the use of an ICC-compatible colormanagement system. The International Color Consortium (ICC) is anindustry consortium which defined open standards for a Color MatchingModule (CMM) at the OS level, and color profiles for the devices andworking space (color space the user edits in).

A color printer destination profile provides a set of device-dependentcolorant values (e.g., CMYK) necessary to produce a given colorcorresponding to a given device-independent color specification (e.g.,L*a*b*). For a 4-color (CMYK) printer, this is a three variable to fourvariable transformation, i.e., transforming L*a*b*→CMYK, which isunderdetermined. As a result, there are many device-dependent colorspecifications for each device-independent color specification. In otherwords, there is more than one CMYK combination that can produce a givenL*a*b*. More combinations are possible when more than 4 colorants areused, e.g., six color CMYKOV. Consequently, in creating a destinationprofile for a given device, it is often necessary to select onedevice-dependent color solution out of the several possible solutionsfor each device-independent color specification. In 4-color printers(CMYK), this selection is often performed by choosing a GCR (GrayComponent Replacement) strategy. GCR is a color strategy which relatesan amount of CMY to an amount of Black (K). This can lead to a 3-to-3transformation which has a unique solution. There are, of course, amultiplicity of GCR strategies that can be chosen. Each strategy isequally valid from a colorimetric viewpoint. Applying a fixed GCRstrategy does not always provide an optimal solution across theavailable output gamut of a particular device.

Image spatial noise defects are an image quality problem that generallypresents itself as two-dimensional color and/or intensityinconsistencies across an area of an image. Examples of image spatialnoise defects are graininess and mottle. Image spatial noise defects areseparate from overall color accuracy defects and refer to variations inthe intensity and/or color produced by a particular device-dependentcolor specification. Addressing image spatial noise defects in printingsystems has been particularly challenging.

Accordingly, what is needed in this art are increasingly sophisticatedsystems and methods for selecting an optimum colorant set from the setof available color combinations for a given N-color color device therebydefining a device-dependent color specification that produces a desireddevice-independent color value while optimizing color image noise andthus improving device performance.

INCORPORATED REFERENCES

International Color Consortium—Profile Specification—Version 4.2.0.0describing image technology, architecture, profile format and structure.(2004).

International Print Quality Standard—ISO/IEC 13660:2001. This standardspecifies device-independent image quality metrics, measurement methods,and analytical procedures to describe the quality of output images fromhardcopy devices and is applicable to human-readable documents composedof binary monochrome images produced from impact printers, non-impactprinters, and copiers.

BRIEF SUMMARY

What is disclosed is a novel system and method for selecting a colorantset from all available color combinations for a given 4-color device toso as to produce a device-dependent color specification for a givendevice-independent color specification that improves overall deviceperformance. For each of a number of selected output colors, color imagespatial noise values for a set of device-dependent color specificationsthat produce the selected output color are iteratively determined. Theset of device-dependent color specifications is generated by varying asubset of colorants in the device-dependent color specifications whilechanging the remaining colorants to maintain the selected output color.A directed search is performed in the iterative process to improve thecolor image spatial noise value, as determined by a color image spatialnoise model, of the device-dependent color specifications thatcorrespond to the selected output color. When an optimum or acceptable(i.e., below a pre-determined threshold) color image spatial noise valueis found, the device-dependent color specification having that colorimage spatial noise value is selected as the mapping for the selecteddevice-independent color specification. Advantageously, the presentmethod utilizes a color image spatial noise function which receivesweighted contributions from several selected color image spatial noiseattributes including mottle and graininess. The present method isreadily extendible to N-color devices, where N≧4.

In one example embodiment, the present method for producingdevice-dependent color specifications for an N-color device, where N≧4,involves the following. First, a selected device-independent colorspecification is received. A current device-dependent colorspecification for a target N-color device, which corresponds to theselected device-independent color specification, is determined. Thefollowing is then iteratively repeated until a termination conditionbased upon the convergence of a transform of the selecteddevice-independent color specification to a device-dependent colorspecification is determined. A set of changed colorant values is createdby changing, by a selected amount, values of a selected subset ofcolorants within the current device-dependent color specification. Amodified device-dependent color specification, which comprises thechanged colorant values for the selected subset of colorants and valuesdetermined, based upon a printer model of the target N-color device, forremaining colorants of the modified device-dependent color specificationis determined so that the modified device-dependent color specificationcontaining the changed colorant values yields the selecteddevice-independent color specification. In the modified device-dependentcolor specification, the remaining colorants are different than theselected subset of colorants. A new color image spatial noise valueassociated with the modified device-dependent color specification isdetermined. The new color image spatial noise value is compared withpreviously determined color image spatial noise values. The amount tochange values of the selected subset of colorant values within thedevice-dependent color specification during a next iteration is adjustedbased upon the comparing of the new color image spatial noise value withpreviously determined color image spatial noise values. A convergence ofthe transform of the selected device-independent color specification toa device-dependent color specification is determined in a manner morefully described herein to produce a resulting device-dependent colorspecification. Once convergence has been determined, the resultingdevice-dependent color specification is stored in a colorcharacterization of the target N-color device as a mapping for theselected device-independent color specification. The colorcharacterization is output. Various embodiments have been disclosed.

Many features and advantages of the above-described method will becomereadily apparent from the following detailed description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 illustrates a device-independent color specification todevice-dependent color specification transformation;

FIG. 2 illustrates a flow diagram of one example embodiment for colorcharacterization model determination process;

FIG. 3 is a continuation of the flow diagram of FIG. 2 with flowprocessing continuing with respect to node A;

FIG. 4 illustrates a flow diagram of an example embodiment of a colorimage spatial noise model determination process which, in theillustrated embodiment determines color image spatial noise metric datafor a particular image output device, such as a color printer orelectronic display, by processing data detected by sensors monitoringoutput images produced by that image output device;

FIG. 5 is a component diagram of an example spatial noisecharacterization model generation system, in accordance with oneembodiment of the present method; and

FIG. 6 illustrates a block diagram of one example embodiment of aspecial purpose computer useful for implementing one or more aspects ofthe present method, as shown and discussed with respect to theabove-described illustrations.

DETAILED DESCRIPTION

What is provided are a system and method for selecting a colorant setfrom available color combinations for a given 4-color device so as toproduce a device-dependent color specification for a givendevice-independent color specification that improves overall deviceperformance. The present method advantageously utilizes a color imagespatial noise defect function which is able to receive weightedcontributions from several selected color image spatial noise defectsincluding mottle and graininess. The present method furtheradvantageously reduces image noise defects, such as mottle andgraininess, by selecting mappings for device-independent colorspecifications to device-dependent color specifications such that theselected device-dependent color specifications create images withreduced image spatial noise defects and therefore reduce, for example,the difficult to address image quality defects of graininess and mottle.

It should be understood that one of ordinary skill in this art would bereadily familiar with many facets of color science such as, but notlimited to, color space, color gamuts, gamut mapping, and other relatedtechniques and algorithms common in the digital document reproductionarts. Additionally, one of ordinary skill would also be familiar withtechniques used for color manipulation and various color transformationprocesses and the systems involved with color imaging. Those of ordinaryskill would be familiar with the text: “Digital Color Imaging Handbook”,CRC Press (2003), ISBN-13: 978-0849-309007, and “Control of ColorImaging Systems: Analysis and Design”, CRC Press (2009), ISBN-13:978-0849-337468, both of which are incorporated herein in their entiretyby reference. One of ordinary skill would also be knowledgeable aboutcomputer science and software and programming techniques and systems andmethods sufficient to implement the functionality and capabilitiesdescribed herein in their own system environments without undueexperimentation.

NON-LIMITING DEFINITIONS

A “Device-Independent Color Space” is any standard color space that iscommonly used to define or describe color, e.g. CIE XYZ, CIE L*a*b*, CIEL*u*v*, sRGB etc.

A “Device-Dependent Color Space” is a color space that is non-standardand cannot be used to commonly define colors without additionalinformation such as the characteristics of the rendering device. Forexample, the commonly used CMYK color space for 4-color printers is adevice-dependent color space since the rendering of a given combinationof CMYK colors could yield very different (device-independent) colorsfrom one model of a printer to another model of a printer. As anotherexample, the non-standard RGB space is also a device-dependent colorspace since the rendering of an RGB image could appear differently fromone model of a CRT monitor to another model of a CRT monitor.

A “color separation” refers to an individual separation corresponding toone of the colorants of a target marking system. For example, there are4 separations: C, M, Y, K for a 4-color CMYK printer. Combinations ofall color separations can be used to produce a range of colors by thetarget marking system.

A “single-separation color” refers to a color specified with only one ofthe color separations for a target marking system. For example, for aCMYK printer, a C-only test patch is a single-separation color testpatch.

A “multi-separation color” refers to a color specified with more thanone of the color separations for a target marking system. For example,for a CMYK printer, a red test color is a multi-separation color testpatch, which uses combinations of M and Y separations at somepre-determined levels, respectively.

A “colorant” refers to the medium used for rendering a particular colorseparation which, in forming a multi-colored image, is combined with oneor more other colorants to achieve image colors throughout the spectrum.Each color separation thus may have its own corresponding colorant.

A “printer model” converts values in a device-dependent color space tovalues in a device-independent color space for a target marking system.A printer model can have the form of a look-up table (LUT) such as a 4-DLUT for CMYK printer, or a parameterized fitted function such as apolynomial that relates inputs in device-dependent color space tooutputs in device-independent color space. For CMYK color space as thetarget device-dependent color space and L*a*b* color space as the outputdevice-independent color space, updating the printer model includes, forexample, for each of a selected number of the data nodes within theprinter model: (a) printing a patch using the specified CMYK components;(b) measuring the color L*a*b* of this printed patch; (c) compiling therelationship between this input CMYK specified and its correspondingmeasured output L*a*b* for each node; and finally (4) storing thesecompiled relationships of all the nodes into a form of LUT. This LUT cannow be used to map any CMYK in device-dependent color space to itspredicated output L*a*b* in the device-independent color space for atarget marking system, and is thus the printer model for this targetmarking system.

A “device-dependent color specification” for a color refers to aparticular combination of values within a device-dependent color spacethat is used to produce a particular value within a device-independentcolor space by a target marking system.

A “profile”, for a given device, is a multi-dimensional color correctionlookup table (LUT) generally comprising a series of nodes in an inputcolor space (L*a*b or XYZ), and device-specific (CMYK) output valuesstored at each node. When the input pixels to be corrected correspond tothe nodes of the LUT, the corresponding device-specific color values areretrieved directly from the LUT. If the pixels are not on the node thenthey are derived via interpolation using a variety of standardizedtechniques, such as, for example, tetrahedral interpolation. In general,a profile is derived from a forward model that maps a device-specific(CMYK) representation to a visual (L*a*b) color representation. Thesemathematical transformations are often embodied as multi-dimensionalLUTs which provide the capability to match the printed color to aproofing device. A multi-dimensional profile LUT has finite nodes forwhich device CMYK values are calculated during profile creation. Ingeneral, a color characterization model is a description of a specificdevice in terms of the transformations required to convertdevice-independent color information to device-dependent color space.

A “storage device” refers to a device or system capable of storingvalues for subsequent retrieval. One embodiment of a storage device is ahard disk, as are well known in the arts, placed in communication with acomputer system or workstation. The term “storage devices” is intendedto include volatile and non-volatile storage such as, for example, RAM,ROM, Cache Memory, CD-ROM, DVD, flash drives, and the like.

“Graininess”, as defined by ISO-13660, is the aperiodic fluctuation ofdensity at a spatial frequency greater than 0.4 cycles per millimeter inall directions. Other definitions, such as with different spatialfrequency ranges and/or measuring fluctuations in different color spaces(e.g. L* rather than density), exist as well. Methods to quantifygraininess are well established. One method is to print and measure atest target containing a gray tone scale from 0% tint (white) to 100%(black) in 10% steps. Tone steps in other colors like cyan, magenta,yellow, red, green, and blue may also be included in the target.Depending on the sample being measured, it might be desirable to performmore than one graininess measurement per patch to reduce errorsintroduced by sample variability. For samples with lower perceivedgraininess, consider making multiple measurements and calculating theaverage reflectance and average graininess values. In one embodiment,graininess is calculated as follows:

${G = {{\mathbb{e}}^{{- 1.8}D} \times {\sum\limits_{f_{n}}{{V\left( f_{n} \right)} \times \sqrt{P^{\prime}\left( f_{n} \right)}}}}},$

where D is the density, V(ƒ_(n)) is visual transfer function as functionof the mean density level and deviation from the mean, and P(ƒ_(n)) isthe power spectrum (compensating for aperture)

The term “Mottle” refers to 2-dimensional spatial noise defects that arerepresented by intensity variations that extend over noticeably largeimage areas and are able to be characterized by low frequency spatialcomponents. Mottle is similar to graininess, but on a larger spatialscale (e.g., >250 μm). Various metrics have been developed by vendors,some proprietary, which are used to determine lightness variation ofmottle. The above-references ISO-13660:2001 defines mottle as theStandard Deviation (STD) of Optical Density (OD) (rather than L*)between 1.27×1.27 mm² and 12.7×12.7 mm² scale over a defined spatialfrequency. Xerox, in one embodiment, defines mottle as STD of L* (plusan OD correction) between 1.1×1.1 mm² and 5.5×5.5 mm² scale.

The term “color image spatial noise” refers to a characteristic of amulti-color printer that characterizes random (non-structured)2-dimensional spatial variations in output intensities for a constantdevice dependent color specification. Color image spatial noise isreflected in spatial noise defects observed in produced color images.Spatial noise is able to be characterized, for example by observingmottle and graininess of a produced output image area that was specifiedto contain a constant device-dependent color specification.

A “color image spatial noise value” refers to a quantity associated witha particular set of values of a device-dependent color specification. Acolor image spatial noise value represents an amount by which a colorarea produced in response to a particular device-dependent colorspecification by a printer or a group of printers will vary across aprinted page.

Reference is now being made to FIG. 1 which illustrates adevice-independent color specification to device-dependent colorspecification transformation shown comprising a plurality of modules.

In one example, a device-independent color specification 102 is providedfor a desired color to be printed. A common example of adevice-independent color specification 102 is a L*a*b* colorspecification that specifies three values for L*, a*, and b*,respectively. Particular values assigned to three color dimensions areable to specify a particular color in a standard device-independentformat that is able to be exchanged among various devices and used topresent a particular color by each device receiving thisdevice-independent color specification. A color characterization model104 is used to transform a received device-independent colorspecification into a device-dependent color specification 106. Variousimage output devices use, for example, more than three color componentsto create a particular output color. A common example is a four coloroutput device, such as a printer, that uses specified amounts of Cyan,Magenta, Yellow, and Black colorant to create a specific output color.Another example is a six color printer that uses specified amounts ofCyan, Magenta, Yellow, Orange, Violet, and Black to create a specificoutput color. The device-independent color specification is defined bythree variable values and these three values are required to betransformed into the four or more colorant values that are to be used bythe color output device to generate the color specified by thedevice-independent color specification. The color characterization model104, which is generated in one embodiment of the present method,provides the transform function to transform any receiveddevice-independent color specification into a device-dependent colorspecification, which is able to contain four or more dimensions. Thedevice-dependent color specification 106 is then provided to an imageoutput device 108. Image output device 108 is able to be any devicecapable of producing a color image in, for example, a hardcopy format oran electronic display format. For example, image output device 108 isable to be a four color printer, a six color printer, a printerutilizing any number of colorants to create color hardcopy outputs. Theimage output device 108 is also able to include an electronic displayable to produce color images to be presented to a viewer.

The image output device of one embodiment produces an output image 110.The form of the output image corresponds to the function of the imageoutput device 108. For example, an image output device that is a colorhardcopy printer will produce a color hardcopy output. An image outputdevice that is a color electronic display will produce a color image onthat electronic display. In one embodiment, a color characterizationmodel generator 114 generates color characterization model 104 byoptimizing the color image spatial noise defect performance of thetransformation between the device-independent color specification 102and the device-dependent color specification 106 using a color printermodel 112 and a color image spatial noise model 116.

The color image spatial noise model 116 in one embodiment hereofincludes separate models to characterize mottle and graininess. Themodel to characterize mottle and the model to characterize graininessare created in one embodiment according to conventional techniques. Inone embodiment, the spatial noise model 116 combines the values ofmottle and graininess for a candidate device-dependent colorspecification to more accurately reflect the perceived image qualityproduced by that candidate. For example, values produced by a model formottle, referred to as an “NMF” model, and a model for graininess,referred to as a “VNHF” model, are combined. From the results of apsychophysical experiment, an empirically determined equation to combinethe output values of these two models is given by the following equationfor a given device-dependent color specification (S):V(S)=−2.31+0.23·NMF(S)+0.67·VNHF(S)−0.013·NMF(S)×VNHF(S)  (1)

where NMF(S) is a mottle metric and VNHF(S) is a graininess metric forthe given device-dependent color specification (S). In this example, thedevice dependent color specification (S) is a particular set of fourvalues of a CMYK device-dependent color specification. TheNMF(S)×VNHF(S) term represents an interaction in the perception of thetwo spatial noise defects. In one embodiment, the NMF model and the VNHFModel are each stored in respective look up tables (LUTs) where valuesfor particular device-dependent color specifications that are notdirectly stored in the LUT are determine by, for example, interpolationbetween stored values.

The color characterization model defines color characterizations thatare used in, for example, printing the resulting device-dependent colorspecification, storing the resulting device-dependent colorspecification to a storage device, updating a lookup table, deriving acolor profile for the device, producing a profile for spot coloremulation, and generating a device-dependent recipe for a spot color.

Example Flow Diagram of One Embodiment

Reference is now made to FIG. 2, which illustrates a flow diagram of oneexample embodiment for a color characterization model determinationprocess.

Color characterization model determination process 200 begins byreceiving, at 204, a selected device-independent color specification asa selected device-independent color specification for which atransformation into a device-dependent color specification is to bedetermined. The selected device-independent color specification is ableto be selected as any of a spot color or a node in the colorcharacterization model for the target device. The selecteddevice-independent color specification is also able to be firstgamut-mapped to the gamut of the target N-color device for which thetransformation is to be determined. The color characterization modeldetermination process 200 continues by determining, at 206, a currentdevice-dependent color specification that corresponds to the selecteddevice-independent color specification. The device-dependent colorspecification can be determined through the use of, for example, adefault color transform for the image output device, such as a defaultprinter profile for the target printer that is specified by an ICC colorprofile.

The color characterization model determination process 200 continues bychanging, at 208, a subset of colorant values in the currentdevice-dependent color specification by a selected amount. The selectedamount is able to be initially configured to a default value. Asdescribed below, the selected amount is adjusted based upon variouscriteria. In one embodiment, the subset of colorant values that arechanged is one color component value of the device-dependent colorspecification. Further embodiments are able to change any number ofcolor component values of the device-dependent color specification. Thevarious embodiments are able to change any number of colorant valueswithin the device-dependent color specification that is fewer than thetotal number of colorants. One example of the present method changes onecolorant in a four colorant device-dependent color specificationconsisting of CMYK values. In one example, the value of the blackcolorant is changed at this step. In further embodiments determiningcolor characterization models for an N-color image output device, thesubset of colorant values within the device-dependent colorspecification that are changed is equal to N−3 colorant values.

The color characterization model determination process 200 continues bydetermining, at 210, the values of the remaining color components thatwill result in generating the selected device-independent colorspecification when they are used in combination with the above changedsubset of color value as a modified device-dependent colorspecification. The combination of the changed subset of colorant values,as changed in step 208, and the determined remaining color componentvalues at step 210 are used as a modified device specific colorspecification. In the example of changing, at 208, one color value inthe device-dependent color specification of a four color printer, theremaining three colorants are determined, at 210, so that thecombination of the new four device-dependent colorant values will stilloutput the same selected device-independent color component as given bythe previous combination of the four device-dependent colorant values.In the example of a four color printer, the device-independent colorspecifications contain three values, thereby providing anunderdetermined relationship to the four color values of adevice-dependent color specification for a four color printer. Thiscombination of four values, the one changed color component value andthe three determined color component values, are then used as thecurrent device-dependent color specification in the followingprocessing.

The color characterization model determination process 200 continues bydetermining and storing, at 212, a new color image spatial noise metricvalue for the modified device-dependent color specification. In oneembodiment, color image spatial noise for the various color componentvalues of the modified device-dependent color component specification isdetermined by a color image spatial noise model 116, as shown anddescribed with respect to the embodiment of FIG. 1, that is stored inone or more look-up tables (LUTs). Color image spatial noise values fordevice-dependent color specification values that lie between valuesstored in the LUTs are able to be interpolated, as is understood bypractitioners of ordinary skill in the art in light of the presentdiscussion. The generation of a color image spatial noise model usingthe actual image output device is described below. Further embodimentsof the present method are able to use any suitable color image spatialnoise model, as is known by practitioners of ordinary skill in therelevant arts.

In step 216, a determination is made whether a termination criterion ismet. If the termination criterion is not met, the process proceeds toadjusting, at step 222, the selected amount to change in the subset ofcolorant values based on a change in color image spatial noise values.After changing the selected amount, the processing returns to changing,at 208, the subset of colorant values in the current device dependentcolor specification by the selected amount.

If a termination criterion is met, at 216, the process continues to step226, where the current device-dependent color specification is stored inthe color characterization model as the device-dependent colorspecification into which the current device-independent colorspecification is to be transformed. The termination criterion, forexample, could be one or more of the following: the color image spatialnoise value reaches a threshold value, the color image spatial noisevalue reaches convergence, or a maximum number of iterations beingreached.

In one example of a termination criterion, a determination is madewhether the new color image spatial noise metric value is below (i.e.,has less variability than) a pre-determined threshold of the color imagespatial noise metric. If this is true, the solution is determined to beacceptable and the modified device-dependent color specification isstored in the color characterization model, at 226, as thedevice-dependent color specification into which the selecteddevice-independent color specification is to be transformed. If this isnot true, the solution is determined to be not acceptable yet and theprocessing continues to step 222.

In another example of a termination criterion, a determination is madewhether the current color image spatial noise metric value is smaller(e.g., by a pre-specified tolerance) than some or all previously storedcolor image spatial noise metric values. If it is not, the solution isdetermined to have converged and the device-dependent colorspecification corresponding to the smallest color image spatial noisemetric value achieved is stored in the color characterization model, at226, as the device-dependent color specification into which the currentdevice-independent color specification is to be transformed. If it is,the solution is determined to have not yet converged and the processingcontinues to step 222.

In yet another example of a termination criterion, a determination ismade whether the maximum iteration count for evaluating device-dependentcolor specifications has been reached. Various embodiments are able toset a maximum number of iterations of device-dependent colorspecifications that are to be evaluated for each device-independentcolor specification. If the maximum number of iterations has not beenreached, the processing returns to step 222. If the maximum number ofiterations has been reached, the process continues to step 226.

In yet another example of a termination criterion, the conditions of allthree criteria mentioned above are tested in each iteration. If any oneof the conditions is met, a termination is determined and the processcontinues to step 226. If none of the three conditions are met, theprocess continues to step 222.

In one embodiment, the amount by which the values of the subset of colorcomponents are adjusted, in step 222, is based on the color imagespatial noise metric value for the current device-dependent colorspecification and the local slopes of the selected subset of thecolorants to the color image spatial noise value e.g. ∂(SpatialNoise)/∂(K). These local slopes can be readily determined from the colorimage spatial noise model (e.g. CMYK→Spatial Noise), for example, bypassing a small perturbation of the selected subset of the colorants tothe color image spatial noise model and observing the changes in colorimage spatial noise value.

After storing, at 226, the modified device-dependent colorspecification, the processing proceeds as described below with regardsto FIG. 3.

Reference is now being made to the flow diagram of FIG. 3 which is acontinuation of the flow diagram of FIG. 2 with flow processingcontinuing with respect to node A.

At 228, a determination is made whether all of the device-independentcolor specification values have been processed. In one embodiment, thenumber and relationships between values of device-independent colorspecifications that are to be processed is manually configured basedupon a desired quality of the color characterization model. If all ofthe device-independent color specifications have not been processed, theprocessing continues by selecting, at 230, the next device-independentcolor specification that is to be processed as the selecteddevice-independent color specification. The processing then returns todetermining, at 206, the device-dependent color specification thatcorresponds to this new selected device-independent color specificationthat is to be used as the current device-dependent color specificationfor subsequent processing. If it is determined that all of thedevice-independent color specifications have been processed, theprocessing ends.

In addition to the image spatial noise constraints, a smoothnessconstraint can be applied. This is advantageous when the colorcharacterization model is used to process images with colors thatsmoothly change from one device independent value to another. In thiscase, the device-dependent values should also change smoothly to avoidcreating unpleasant contours in the printed image. One way of doing thisis to process the nodes of the color characterization model using regiongrowing techniques. After processing one node, the next node to beprocessed is chosen to be adjacent, in LAB space, to the alreadyprocessed nodes. A smoothness metric term, A(cmyk) for example, alongwith a corresponding weight is able to be added to the sum in Eq. 1. Theterm would be large when the difference between the chosen CMYK valueand the average CMYK values of the already determined nearby nodes waslarge. The calculation can be initialized, for example, by starting offwith the node corresponding to the color white, since there is generallyonly one way of making that color.

It should be appreciated that any of the values determined above, orinterim values required for any of the above-described determinations,may be stored in any of the embodiments of a storage device, as definedherein.

Example Flow Diagram of Color Image Spatial Noise Model Determination

Reference is now made to FIG. 4, which illustrates a flow diagram of anexample embodiment of a color image spatial noise model determinationprocess 400 which, in the illustrated embodiment determines color imagespatial noise metric data for a particular image output device, such asa color printer or electronic display, by processing data detected bysensors monitoring output images produced by that image output device.

In one embodiment, a color image spatial noise function for the gamut ofan image output device is determined by making actual color imagespatial noise measurements for a relatively small number of colors tocharacterize the color image spatial noise of a finite number of pointsin the color gamut of the image output device. The color image spatialnoise values for any color combination is then able to be interpolatedor extrapolated for color combinations for which measured data is notavailable. In one example, the color image spatial noise is measured forcolors that are defined by a number of device-dependent colorspecifications that are chosen to produce, for example, four to eightdifferent color intensity levels for each color component of thedevice-dependent color specification. In an example of a four colorprinter, characterizing the color image spatial noise by measuringactual output using four different color component intensity values foreach of the four colors yields 4⁴ different colors, or 256 colors, forwhich color image spatial noise is measured. Color image spatial noisefor these 256 colors is measured by determining the variation of colorsactually produced at various locations on the same page as well asacross multiple pages for each of these 256 device-dependent colorspecifications.

The color image spatial noise model determination process 400 begins bygenerating, at 402, a color image spatial noise test pattern to be usedto characterize color image spatial noise. In one embodiment, the colorimage spatial noise of an image output device is determined by actualmeasurement of image output color image spatial noise for a set ofdevice-dependent color specification values contained in the color imagespatial noise test pattern. The set of device-dependent colorspecification values included in the color image spatial noise testpattern is defined, for example, to include all combination ofdevice-dependent color specifications that have a specified number ofdifferent, equally spaced, values in each color dimension.

The color image spatial noise model determination process 400 continuesby producing, at 404, the color image spatial noise test pattern in anoutput image produced by an image output device being characterized. Theprocess continues by determining, at 406, spatial noise defects for eachof the different device-dependent color specification values within theproduced color image spatial noise test pattern. In one embodiment, adigital image is captured of the produced color image spatial noise testpattern that is processed by analytical software to determine graininessand mottle values for each device-dependent color specification. In oneembodiment, 2-dimensional spatial noise defects are analyzed to produceseparate models that characterize mottle and graininess of the producedcolor image spatial noise test pattern. A 2-dimensional spatial noisedefect model is determined, at 408. In one embodiment, the 2-dimensionalspatial noise defect model includes components that characterize mottleand graininess. Techniques to determine a model to characterize outputimage mottle and graininess are known by practitioner of ordinary skillin the relevant arts in light of the present discussion.

The determined 2-dimensional spatial noise defect model is then stored,at 410, in a look up table and serves as the basis of a color imagespatial noise model 116 of FIG. 1.

It should be understood that the flow diagrams of FIGS. 2, 3 and 4, areintended to be illustrative. Other operations may be added, modified, orconsolidated. Variations thereof are intended to fall within the scopeof the appended claims.

Example Color Characterization Model Generation System

Reference is now made to FIG. 5, which is a component diagram of aspatial noise characterization model generation system 500, inaccordance with one embodiment of the present method, and showncomprising a plurality of modules.

The spatial noise characterization model generation system 500 includesa color printer 504 that is able to produce color images in the form ofprinted output 506. In one embodiment, color printer 504 is a productionprinter generally used to create user documents from user files 502.User files, in one embodiment, include color images specified in adevice-independent color specification, such as in a Lab format. In oneembodiment, the color printer 504 has associated equipment to supportcharacterization of the color image spatial noise performance of thecolor printer in order to update a color image spatial noise model forthat color printer. Based upon this updated color image spatial noisemodel, updated color characterization models are able to be created toprovide improved color output performance.

The color characterization model generation system 500 includes imagesensing equipment capable of detecting the color produced on an outputimage to support determining and/or updating a color image spatial noisemodel of an image output system. Image sensor 510 is positioned tocapture a digital representation of the color image contained in theprinted output 506. An alternative embodiment includes a scanner 512. Inone embodiment, the printed output 506 of the color printer 504 isphysically transferred to the scanner to enable scanning of the producedcolor images on the printed output. In the various embodiments, adigital representation of the output image, such as is captured by thesensor 510 or scanner 512, is provided to a profile generator 514. Inone embodiment, the profile generator further provides a color imagespatial noise test pattern to color printer 504 to cause a suitablepattern to be generated by that color printer. The profile generator, ofone embodiment, performs the above-described processing to produce acolor image spatial noise model for the color printer. The colorcharacterization model generator uses that color image spatial noisemodel to produce a color characterization model that definesdevice-independent color specification to device-dependent colorspecification transformations that are based upon optimum color imagespatial noise characteristics determined for the particular colorprinter.

It should be appreciated that the various modules of the schematic ofthe embodiments of the component diagrams of FIGS. 1 and 5, designate acomponent of a system which may comprise software and/or hardwaredesigned to perform a function. A plurality of modules may collectivelyperform one or more functions. A module may have specialized processorscapable of reading machine executable program instructions. A module maycomprise a single piece of hardware such as an ASIC, electronic circuit,or special purpose computer system such as is shown in FIG. 6. Aplurality of modules may be executed by either a single special purposecomputer system or a plurality of special purpose computer systems inparallel. Connections between modules include both physical and logicalconnections. Modules may further include one or more software/hardwaremodules which may further comprise an operating system, drivers, devicecontrollers, and other apparatuses some or all of which may be connectedvia a network.

Schematic of Example Special Purpose Computer

Reference is now made to FIG. 6 which illustrates a block diagram of oneexample special purpose computer useful for implementing one or moreaspects of the present method. Such a system could be implemented as aseparate computer system, an electronic circuit, or an ASIC, forexample. The nature of the implementation will depend on the processingenvironment wherein the present method finds its intended uses. Thespecial purpose computer system would execute machine readable programinstructions for performing various aspects of the embodiments describedherein with respect to the embodiments of FIGS. 1 and 5, and the flowdiagram of FIGS. 2, 3 and 4.

Special purpose computer system 600 includes processor 606 for executingmachine executable program instructions for carrying out all or some ofthe present method. The processor is in communication with bus 602. Thesystem includes main memory 604 for storing machine readableinstructions. Main memory may comprise random access memory (RAM) tosupport reprogramming and flexible data storage. Buffer 666 stores dataaddressable by the processor. Program memory 664 stores machine readableinstructions for performing the present method. A display interface 608forwards data from bus 602 to display 610. Secondary memory 612 includesa hard disk 614 and storage device 616 capable of reading/writing toremovable storage unit 618, such as a floppy disk, magnetic tape,optical disk, etc. Secondary memory 612 may further include othermechanisms for allowing programs and/or machine executable instructionsto be loaded onto the processor. Such mechanisms may include, forexample, a storage unit 622 adapted to exchange data through interface620 which enables the transfer of software and data. The system includesa communications interface 624 which acts as both an input and an outputto allow data to be transferred between the system and external devicessuch as a color scanner (not shown). Example interfaces include a modem,a network card such as an Ethernet card, a communications port, a PCMCIAslot and card, etc. Software and data transferred via the communicationsinterface are in the form of signals. Such signal may be any ofelectronic, electromagnetic, optical, or other forms of signals capableof being received by the communications interface. These signals areprovided to the communications interface via channel 626 which carriessuch signals and may be implemented using wire, cable, fiber optic,phone line, cellular link, RF, memory, or other means known in the arts.

Terms such as, computer program medium, computer readable medium,computer executable medium, and computer usable medium are used hereinto generally refer to a machine readable media such as main memory,secondary memory, removable storage device such as a hard disk, andcommunication signals. Such computer program products are means forcarrying instructions and/or data to the computer system or device. Suchcomputer program products may include non-volatile memory, such as afloppy disk, hard drive, memory, ROM, RAM, flash memory, disk memory,and other storage useful for transporting machine readable programinstructions for executing the present method. It may further include aCD-ROM, DVD, tape, cassette, or other digital or analog media, capableof having embodied thereon one or more logical programming instructionsor other machine executable codes or commands that implement andfacilitate the function, capability, and methods disclosed herein.

It should be understood that one or more aspects of the present methodare intended to be incorporated in an article of manufacture, includingone or more computer program products. The article of manufacture may beincluded on a storage device readable by a machine architecture,xerographic system, color management or other image processing system,any of which capable of executing program instructions containing thepresent method. Such an article of manufacture may be shipped, sold,leased, or otherwise provided separately either alone or as part of anadd-on, update, upgrade, download, or product suite by the assignee or alicensee hereof as part of a computer system, xerographic system,document processing system, image processing system, color managementsystem, operating system, software program, plug-in, DLL, or a storagedevice.

It will be appreciated that the above-disclosed features and functionand variations thereof may be desirably combined into many otherdifferent systems or applications. Various presently unforeseen orun-anticipated alternatives, modifications, variations, or improvementsmay become apparent and/or subsequently made by those skilled in the artwhich are also intended to be encompassed by the appended claims. Theembodiments set forth above are considered to be illustrative and notlimiting. Various changes to the above-described embodiments may be madewithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A method for producing device-dependent colorspecifications for an N-color device, where N is greater than three, themethod comprising: receiving, with a processor, a selecteddevice-independent color specification; determining, with the processor,a current device-dependent color specification for a target N-colordevice, the current device-dependent color specification correspondingto the selected device-independent color specification; repeating, withthe processor, until a termination condition is reached: creating a setof changed colorant values by changing, by a selected amount, values ofa selected subset of colorants within the current device-dependent colorspecification; determining, with the processor, a modifieddevice-dependent color specification, the modified device-dependentcolor specification comprising the changed colorant values for theselected subset of colorants and values determined, based upon a printermodel of the target N-color device, for remaining colorants of themodified device-dependent color specification so that the modifieddevice-dependent color specification containing the changed colorantvalues yields the selected device-independent color specification, theremaining colorants being different from the selected subset ofcolorants; determining, with the processor, a new color image spatialnoise value associated with the modified device-dependent colorspecification; adjusting, with the processor, based upon the new colorimage spatial noise value, the amount to change values of the selectedsubset of colorant values within the device-dependent colorspecification during a next iteration; storing, with the processor in acolor characterization of the target N color device, the resultingdevice-dependent color specification as a mapping for the selecteddevice-independent color specification; and outputting the colorcharacterization from the processor.
 2. The method of claim 1, whereinthe termination condition is based upon at least one of determining thatthe new color image spatial noise value is below a pre-determinedthreshold, determining that the new color image spatial noise value hasconverged, and performing a pre-defined number of iterations of therepeating.
 3. The method of claim 1, wherein the currentdevice-dependent color specification and the modified device-dependentcolor specification each comprises N color dimensions, and wherein theselected subset of colorant values in the modified device-dependentcolor specification comprises N−3 colorant values.
 4. The method ofclaim 1, wherein the selected device-independent color specification isany of: a spot color, and a node in the color profile for the device. 5.The method of claim 1, further comprising using the colorcharacterization to perform any of: printing the resultingdevice-dependent color specification; storing the resultingdevice-dependent color specification to a storage device; updating alookup table; deriving a color profile for the device; producing aprofile for spot color emulation; and generating a device-dependentrecipe for a spot color.
 6. The method of claim 1, wherein the selecteddevice-independent color specification is first gamut-mapped to thegamut of the target N-color device.
 7. The method of claim 1, whereindetermining a new color image spatial noise value associated with themodified device-dependent color specification comprises determining acolor image spatial noise value by interpolation from a look-up tablecontaining a respective color image spatial noise value corresponding toeach device-dependent color specification within a set ofdevice-dependent color specifications.
 8. The method of claim 7, whereinthe look-up table is generated by: determining a color image spatialnoise value of a target N-color device for each color specificationwithin the set of device-dependent color specifications; and storingeach of the device-dependent color specification and the associatedrespective color image spatial noise value in the look-up table.
 9. Themethod of claim 1, wherein determining the new color image spatial noisevalue comprises calculating a weighted function of at least oneparameter each having a respective weighting factor, the at least oneparameter comprising at least one of: output image mottle and outputimage graininess.
 10. The method of claim 9, wherein the respectiveweighting factors are based on at least one of a user input, anautomatic calculation based on analysis of a candidate print job, andpsychophysical data.
 11. The method of claim 1, wherein determining acurrent device-dependent color specification comprises obtaining thecurrent device-dependent color specification corresponding to theselected device-independent color from a printer model of the targetN-color device.
 12. The method of claim 11, wherein the printer model isspecified by an ICC color profile.
 13. A system for producingdevice-dependent color specifications for an N-color device, where N isgreater than three, the system comprising: a memory; a storage mediumfor storing data; and a processor in communication with said storagemedium and said memory, said processor executing machine readableinstructions for performing the method of: receiving a selecteddevice-independent color specification; determining a currentdevice-dependent color specification for a target N-color device, thecurrent device-dependent color specification corresponding to theselected device-independent color specification; repeating until atermination condition is reached: creating a set of changed colorantvalues by changing, by a selected amount, values of a selected subset ofcolorants within the current device-dependent color specification;determining a modified device-dependent color specification, themodified device-dependent color specification comprising the changedcolorant values for the selected subset of colorants and valuesdetermined, based upon a printer model of the target N-color device, forremaining colorants of the modified device-dependent color specificationso that the modified device-dependent color specification containing thechanged colorant values yields the selected device-independent colorspecification, the remaining colorants being different from the selectedsubset of colorants; determining a new color image spatial noise valueassociated with the modified device-dependent color specification;adjusting, based upon the new color image spatial noise value, theamount to change values of the selected subset of colorant values withinthe device-dependent color specification during a next iteration;storing, in a color characterization of the target N color device, theresulting device-dependent color specification as a mapping for theselected device-independent color specification; and outputting thecolor characterization.
 14. The system of claim 13, wherein thetermination condition is based upon at least one of determining that thenew color image spatial noise value is below a pre-determined threshold,determining that the new color image spatial noise value has converged,and performing a pre-defined number of iterations of the repeating. 15.The system of claim 13, wherein the current device-dependent colorspecification and the modified device-dependent color specification eachcomprises N color dimensions, and wherein the selected subset ofcolorant values in the modified device-dependent color specificationcomprises N−3 colorant values.
 16. The system of claim 15, whereindetermining a new color image spatial noise value associated with themodified device-dependent color specification comprises determining acolor image spatial noise value by interpolation from a look-up tablecontaining a respective color image spatial noise value corresponding toeach device-dependent color specification within a set ofdevice-dependent color specifications.
 17. The system of claim 16,wherein the look-up table is generated by: determining a color imagespatial noise value of a target N-color device for each colorspecification within the set of device-dependent color specifications;and storing each of the device-dependent color specification and theassociated respective color image spatial noise value in the look-uptable.
 18. The system of claim 13, wherein determining the new colorimage spatial noise value comprises calculating a weighted function ofat least one parameter, the at least one parameter comprising at leastone of: mottle and graininess.
 19. The system of claim 18, wherein therespective weighting factors are based on at least one of a user input,an automatic calculation based on analysis of a candidate print job, andpsychophysical data.
 20. A method for producing device-dependent colorspecifications for an N-color device, where N is greater than three, themethod comprising: producing, with the target N-color device, aplurality of color image spatial noise test patterns, each color imagespatial noise test pattern specifying a plurality of device-dependentcolor specifications; determining, with a sensor, the value of theproduced 2-dimensional image noise defects within each of the pluralityof color image spatial noise test patterns produced by the targetN-color device; determining, based on the value of the produced2-dimensional image noise defects within each of the plurality of colorimage spatial noise test patterns as determined by the determining, acolor image spatial noise model of the target N-color device; receiving,with a processor, a selected device-independent color specification;determining, with the processor, a current device-dependent colorspecification for a target N-color device, the current device-dependentcolor specification corresponding to the selected device-independentcolor specification; repeating, with the processor, until a terminationcondition is reached: creating a set of changed colorant values bychanging, by a selected amount, values of a selected subset of colorantswithin the current device-dependent color specification; determining,with the processor, a modified device-dependent color specification, themodified device-dependent color specification comprising the changedcolorant values for the selected subset of colorants and valuesdetermined, based upon a printer model of the target N-color device, forremaining colorants of the modified device-dependent color specificationso that the modified device-dependent color specification containing thechanged colorant values yields the selected device-independent colorspecification, the remaining colorants being different from the selectedsubset of colorants; determining, with the processor, a new color imagespatial noise value associated with the modified device-dependent colorspecification; adjusting, with the processor, based upon the new colorimage spatial noise value, the amount to change values of the selectedsubset of colorant values within the device-dependent colorspecification during a next iteration; storing, with the processor in acolor characterization of the target N color device, the resultingdevice-dependent color specification as a mapping for the selecteddevice-independent color specification; and outputting the colorcharacterization from the processor.